A data-driven multi-scale constitutive model of concrete material based on polynomial chaos expansion and stochastic damage model

Nonlinearity and randomness are two intrinsic characteristics of the mechanical behavior of concrete material. The structural response under large excitation can barely be predicted without considering these two characteristics. Brilliant works have been done for decades in the material science and computational stochastic mechanics. However, the existed numerical methods are usually parameter dependent and the key mechanical properties of concrete material are determined by empirical recognition. Therefore, in this paper, a data-driven multi-scale constitutive model is proposed for representing the mechanical behavior of concrete material based on the polynomial chaos expansion and stochastic damage model. Several groups of compressive stress-strain data of concrete material are applied to train the proposed model. By cross validation of the prediction and the concrete stress-strain experimental data, the proposed model is firstly verified to have a robust performance to gain accurate prediction results. Afterwards, the proposed method is compared with a neural network method, the results shows that the proposed method is more robust and accurate than the neural network method.

Automated summary

This paper proposes a data-driven multi-scale constitutive model for representing the mechanical behavior of concrete material. The model is trained using multiple sets of experimental data and validated through cross validation, demonstrating its robustness and accuracy.